Evaluating Extreme Rainfall Changes over Taiwan Using a Standardized Index

Abstract

The annual daily maximum precipitation (rx1day) is widely used to represent extreme events and is an important parameter in climate change studies. However, the climate variability in rx1day is sensitive to outliers and has difficulty representing the characteristics of large areas. We propose to use the probability index (PI), based on the cumulative density function (CDF) of a generalized extreme value (GEV) distribution to fit and standardize the rx1day to represent extreme event records in this study. A good correlation between the area-averaged PIs of the observed stations and those of the gridded dataset can be found over Taiwan. From the past PI records, there is no distinct trend in western Taiwan before the end of the 20 th century, but a climate regime change happened during 2002 -2003. The dual change effects from both the variance and linear trend of extreme events are identified over the northeastern and southern parts of Taiwan, along with the island's central and southern regions, showing different abrupt changing trends and intensity. The PI can also be calculated using climate projection data to represent the characteristics of future extreme changes. The climate variability of PIs on the present (ALL) and future (RCP4.5 and RCP8.5) scenarios were evaluated using the 16 Couple Model Intercomparison Projects Phase-5 models (CMIP5). The simulated present fluctuations in PIs are smaller than those of actual observations. In the 21 st century, the RCP8.5 scenario shows that the PI significantly increases by 10% during the first half of the century, and 14% by the end of the century.

Extreme Rainfall in Taiwan: Seasonal Statistics and Trends

“…Taiwan's annual island-average extreme rainfall has been increasing (Chen et al 2007;Shiu et al 2009;Tung et al 2016;Wu et al 2019), especially since the year 2000 (Chang et al 2013;Tung et al 2016;Liang et al 2017). In this next section, we will follow two complementary approaches for defining and calculating ER trends.…”

Ambient Fungal Spore Concentration in a Subtropical Metropolis: Temporal Distributions and Meteorological Determinants

“…After referring to Khattab and Levetin (2008) regarding sampling height difference and fungal spore concentration, the Aspergillus/Penicillium concentration in the present study was expected to be lower because of the higher sampling height (15 m above the ground) than that used by Chen et al (2.5 m above the ground); however, our results revealed a higher Aspergillus/Penicillium concentration. The possible reasons for this result include different sampling locations, nearby land utilization, and changes in meteorological and environmental conditions over the past few years, as has been reported by many studies (Ding et al, 2016;Kuo et al, 2016;TCCIP, 2016;Tung et al, 2016).…”

The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform: A Decade of Climate Research

“…Precipitation is one of the critical parameters that climate change will influence and that will induce risk, hence the following were studied in great detail in Taiwan: the land-sea breeze system that that facilitates the formation of diurnal rainfall events [34][35][36], and seasonal monsoon events, including Madden-Julian oscillation impacts on winter rainfalls, and the East Asian summer monsoon (aka Meiyu), under different climate scenarios [37,38]. The preliminary study of watershed typhoon rainfalls and extreme precipitation under climate change found that the 24-h duration precipitation depth for 20-year and 100-year extreme events are likely to increase, suggesting concerns about the current hydrologic designs of critical infrastructures [39][40][41][42]. The secondary effects of precipitation on several factors, such as landslides, where critical precipitation is a characteristic that triggers large-scale landslide events, were studied and considered in conjunction in order to understand the cause/effect relationships; they were also impact assessed using climate projection [43][44][45][46][47].…”